nrows = int(nt / tw / ncols) testsize = 0.3 seed = 20180319 channel = 1 length = int(tw / tinc) classes = 2 cbar_binary = ListedColormap(["darkgray", "yellow"]) s1_labels, labels_attr = f.gather_to_label(s1, tw, tinc, thre=0.2) #np.savetxt("out/micro_labels_win10.csv", s1_labels, delimiter=',') s1_labels = pd.read_csv("out/micro_labels_win10.csv", header=None) s1_labels = np.array(s1_labels) fig, ax = f.labels_plot(s1_labels) ax.set_yticks(np.arange(0, int(nt / tw) + 1, 5) - 0.5) ax.set_yticklabels(((np.arange(0, int(nt / tw) + 1, 5) - 0.5) * tw + tw / 2) * dt) ax.set_ylabel('Time (s)', fontsize=13) #fig.savefig('fig/micro_labels.pdf', dpi=200) #%% training dataset for MLP, CNN, and CWT-CNN traces_step = 4 traces_train = np.arange(0, s1.shape[1], traces_step) X = np.zeros((int(len(traces_train) * nt / tw), int(tw / tinc))) Xcwt = np.zeros( (int(len(traces_train) * nt / tw), len(freq_index), int(tw / tinc))) Y = np.zeros((int(len(traces_train) * nt / tw), ))
#%% plot the gather and labels '''generate labels for gather''' #f.gather_plot(Obs, tw) #Obs_labels, labels_attr_rms = f.gather_to_label(Obs, tw, tinc, thre=0.035) #Obs_labels = labels #np.savetxt("out/Obs_labels_win50.csv", Obs_labels, delimiter=',') Obs_labels = pd.read_csv("out/Obs_labels_win50_m.csv", header=None) Obs_labels = np.array(Obs_labels) #f.gather_plot(Obs, tw, savepath='fig/gather_labels.png', # plot_label=True, labels=Obs_labels) fig, ax = f.labels_plot(Obs_labels) ax.set_yticks(np.arange(0, nt / tw + 1, 400 / tw)) ax.set_yticklabels(np.arange(0, nt + 1, 400) * dt) ax.set_xticks(np.arange(0, 967, 100)) ax.set_xticklabels((np.arange(0, 967, 100) / 10).astype(int)) #fig.savefig('fig/obs_label', dpi=200) #%% training dataset for MLP, CNN, and CWT-CNN traces_step = 80 traces_train = np.arange(0, 967, traces_step) X = np.zeros((int(len(traces_train) * nt / tw), int(tw / tinc))) Xcwt = np.zeros((int(len(traces_train) * nt / tw), 15, int(tw / tinc))) Y = np.zeros((int(len(traces_train) * nt / tw), )) for i in traces_train: itrace = Obs[:, i]